Game Development Reference
In-Depth Information
Chapter 13
Affective Educational
Games and the Evolving
Teaching Experience
Karla Muñoz
University of Ulster, UK
Paul Mc Kevitt
University of Ulster, UK
Tom Lunney
University of Ulster, UK
Julieta Noguez
Tecnológico de Monterrey, Mexico
Luis Neri
Tecnológico de Monterrey, Mexico
ABSTRACT
Teaching methods must adapt to learners' expectations. Computer game-based learning environments
enable learning through experimentation and are inherently motivational. However, for identifying when
learners achieve learning goals and providing suitable feedback, Intelligent Tutoring Systems must be
used. Recognizing the learner's affective state enables educational games to improve the learner's ex-
perience or to distinguish relevant emotions. This chapter discusses the creation of an affective student
model that infers the learner's emotions from cognitive and motivational variables through observ-
able behavior. The control-value theory of 'achievement emotions' provides a basis for this work. A
Probabilistic Relational Models (PRMs) approach for affective student modeling, which is based on
Dynamic Bayesian Networks, is discussed. The approach is tested through a prototyping study based
on Wizard-of-Oz experiments and preliminary results are presented. The affective student model will be
incorporated into PlayPhysics, an emotional game-based learning environment for teaching Physics.
PRMs facilitate the design of student models with Bayesian Networks. The effectiveness of PlayPhysics
will be evaluated by comparing the students' learning gains and learning efficiencies.
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